An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

Arrospide, Arantzazu; Forné, Carles; Rué, Montse; Torà, Núria; Mar, Javier; Baré, Marisa

doi:10.1186/1471-2407-13-587

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…The BC screening programme in the Basque Country (BCSPBC) has invited more than 400,000 women since 1996. The risk factor profile of the Basque women population would be similar to the screened women population in Catalonia described by Arrospide et al [ 5 ].…”

Section: Introductionsupporting

confidence: 66%

Evaluation of health benefits and harms of the breast cancer screening programme in the Basque Country using discrete event simulation

et al. 2015

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BackgroundSince the breast cancer screening programme in the Basque Country (BCSPBC) was started in 1996, more than 400,000 women aged 50 to 69 years have been invited to participate. Based on epidemiological observations and simulation techniques it is possible to extend observed short term data into anticipated long term results. The aim of this study was to assess the effectiveness of the programme through 2011 by quantifying the outcomes in breast cancer mortality, life-years gained, false positive results, and overdiagnosis.MethodsA discrete event simulation model was constructed to reproduce the natural history of breast cancer (disease-free, pre-clinical, symptomatic, and disease-specific death) and the actual observed characteristics of the screening programme during the evaluated period in the Basque women population. Goodness-of-fit statistics were applied for model validation. The screening effects were measured as differences in benefits and harms between the screened and unscreened populations. Breast cancer mortality reduction and life-years gained were considered as screening benefits, whereas, overdiagnosis and false positive results were assessed as harms. Results for a single cohort were also obtained.ResultsThe screening programme yielded a 16 % reduction in breast cancer mortality and a 10 % increase in the incidence of breast cancer through 2011. Almost 2 % of all the women in the programme had a false positive result during the evaluation period. When a single cohort was analysed, the number of deaths decreased by 13 %, and 4 % of screen-detected cancers were overdiagnosed. Each woman with BC detected by the screening programme gained 2.5 life years due to early detection corrected by lead time.ConclusionsFifteen years after the screening programme started, this study supports an important decrease in breast cancer mortality due to the screening programme, with reasonable risk of overdiagnosis and false positive results, and sustains the continuation of the breast cancer screening programme in the Basque population.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1700-4) contains supplementary material, which is available to authorized users.

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Section: Introductionsupporting

confidence: 66%

Evaluation of health benefits and harms of the breast cancer screening programme in the Basque Country using discrete event simulation

et al. 2015

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“…BCRAT has been extensively evaluated and is currently recommended for predicting breast cancer risk for US women undergoing mammographic screening (42,43). Whereas some studies found no evidence for miscalibration of BCRAT (44)(45)(46)(47), others reported underestimation (48)(49)(50) or overestimation (47,(51)(52)(53) of risk. Some studies reported improved calibration when using incidence and mortality rates from the same country and time period (49,50,53).…”

Section: Discussionmentioning

confidence: 94%

“…Second, we assessed model calibration by deciles of expected absolute risk and by the relative risk score. The former is commonly used in validation studies (5,(44)(45)(46)48,53,54) because absolute risk is the relevant measure for clinical or public health applications. However, strong dependence of absolute risk on age makes the differences in model performance because of other risk factors less evident than comparisons using the relative risk score, which does not include age.…”

Section: Discussionmentioning

confidence: 99%

Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

Choudhury

Hurson

Brook

et al. 2019

JNCI: Journal of the National Cancer Institute

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Background External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. Methods Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35–74 years. Risk projections in a target population of US white non-Hispanic women age 50–70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). Results The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. Conclusions iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.

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“…The Complement of data in risk assessment of precise cancer screening is an important factor (26). Also, the meaningful use of data for personalized protocols is essential.…”

Section: Introductionmentioning

confidence: 99%

Development of Information Road map for Personalized Colorectal Cancer Screening in Iran

Maserat

Mohammadzadeh

Zali

2020

Preprint

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Background A data-driven colorectal cancer screening strategy based on personalized approach can improve health outcomes, facilitate early stratification of at-risk patients and reduce health care costs. This study aims to develop an information road map for personalized colorectal cancer screening in Iran. Methods This study is a Mix-Method Research (MMR) which consisted of three phases: phase I, development of a checklist with 275-items for assessing required data elements of personalized colorectal cancer screening; phase II, situational analysis of colorectal cancer screening dataset according to the checklist; phase III, development of national information road map for personalized colorectal cancer screening with in-depth interview and focus groups. Results Personalized datasets of colorectal cancer screening were defined in four dimensions, including clinical dataset (5 sub-dimensions, 162 items), genetic dataset (2 sub-dimensions, 67 items), demographic dataset (1 sub-dimension, 6 items) and a social determinant dataset (3 sub-dimensions, 40 items). The next step data elements of colorectal cancer screening based on personalized datasets were analyzed. Of the 275-items, only 96 items are recorded. Only 17.8% of clinical dataset of screening program were entered. The highest data elements of clinical dimension were related to pathological datasets (53.6%) in the present screening program. The lowest data elements of the clinical dimensions were related to the clinical history dataset (3.4%). 73% of pedigree data elements and 15.33% of social determinant datasets were entered. In the final step, a national information road map of personalized CRC screening with 6 layers (information leadership, personalized datasets, data integration, data architecture, data descriptor, and screening program layers) was developed. Conclusion Personalized screening based on integration dataset play a key role for the successful implementation of the screening program. Eliminating data deficiencies can improve the quality of documentation and may lead to improved screening performance. Therefore, standard datasets and indicators can help to identify information gaps and facilitate precise decision-making. Entering data was inadequate and poor in this study. Implementation of national road map can assist to improve the quality of data in personalized screening.

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An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

Cited by 10 publications

References 33 publications

Evaluation of health benefits and harms of the breast cancer screening programme in the Basque Country using discrete event simulation

Evaluation of health benefits and harms of the breast cancer screening programme in the Basque Country using discrete event simulation

Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

Development of Information Road map for Personalized Colorectal Cancer Screening in Iran

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